Railway Point Machines Research Articles

Diagnosis for railway point machines using novel derivative multi-scale permutation entropy and decision fusion based on vibration signals

Abstract Railway point machines (RPMs) are safety-critical pieces of equipment closely related to train operation safety. Due to their high failure rate, it is urgent to develop an effective diagnosis method for RPMs. Considering the easy-to-collect and anti-interference characteristics of vibration signals, this paper develops a vibration-based diagnosis method. First, to address the difficulty of multi-scale permutation entropy in characterizing the fault information contained in the derivatives of the raw signal, a novel feature named derivative multi-scale permutation entropy is designed, which can further complete the fault information of RPMs. Second, to further improve the diagnosis accuracy of support vector machines, a decision fusion strategy based on three feature sets is developed, which can further improve the diagnosis accuracy, especially in the normal-reverse direction. Finally, the effect and superiority of the proposed method are verified based on the collected vibration signals from Xi’an Railway Signal Co.,Ltd by experiment comparisons. The diagnosis accuracies of reverse-normal and normal-reverse directions reach 99.43% and 100% respectively, indicating its superiority.

Dual-input robust diagnostics for railway point machines via audio signals

ABSTRACT Railway Point Machine (RPM) is a fundamental component of railway infrastructure and plays a crucial role in ensuring the safe operation of trains. Its primary function is to divert trains from one track to another, enabling connections between different lines and facilitating route selection. By judiciously deploying turnouts, railway systems can provide efficient transportation services while ensuring the safety of passengers and cargo. As signal processing technologies develop rapidly, taking the easy acquisition advantages of audio signals, a fault diagnosis method for RPMs is proposed by considering noise and multi-channel signals. The proposed method consists of several stages. Initially, the signal is subjected to pre-processing steps, including cropping and channel separation. Subsequently, the signal undergoes noise addition using the Random Length and Dynamic Position Noises Superposition (RDS) module, followed by conversion to a greyscale image. To enhance the data, Synthetic Minority Oversampling Technique (SMOTE) module is applied. Finally, the training data is fed into a Dual-input Attention Convolutional Neural Network (DIACNN). By employing various experimental techniques and designing diverse datasets, our proposed method demonstrates excellent robustness and achieves an outstanding classification accuracy of 99.73%.

A Sequential and Asynchronous Federated Learning Framework for Railway Point Machine Fault Diagnosis With Imperfect Data Transmission

A Sequential and Asynchronous Federated Learning Framework for Railway Point Machine Fault Diagnosis With Imperfect Data Transmission

Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

Vibration-Based Fault Diagnosis for Railway Point Machines Using VMD and Multiscale Fluctuation-Based Dispersion Entropy

Research on intelligent fault diagnosis for railway point machines using deep reinforcement learning

Abstract The advanced diagnosis of faults in railway point machines is momentous to ensure the efficient and stable operation of the turnout conversion system. Numerous mature deep learning methods have been extensively applied in this domain. While robust perception has yielded excellent diagnostic outcomes, the deficiency in decision-making capability has led to a lack of overall intelligence. To deal with this, this study proposes an end-to-end deep reinforcement learning (DRL) framework for diagnosing faults in railway point machines. Firstly, a one-dimensional convolutional neural network (1DCNN) is used for the automatic extraction of features from the current signal. Subsequently, the deep Q network (DQN) algorithm is introduced as the core of the diagnostic framework. This involves designing an interactive environment for fault classification and optimizing the agent training network. Finally, leveraging fault data, the agent and the environment engage in continuous interactive learning to produce the ideal classification policy. Multiple comparative experiments are conducted to validate the proposed method. The results demonstrate that the diagnostic accuracy reaches 98.41%, and the average accuracy after many iterations is as high as 99.12%. Notably, this research introduces a creative application of DRL to address the challenge of diagnosing faults in railway point machines. The incorporation of decision thought effectively enhances the intelligence of fault diagnosis.

Investigating the Significance of 4.0 Digital Transformation on the Improvement of Railway Transport System in South Africa.

Numerous studies have been conducted to define and address the challenges and benefits linked to Industry 4.0 including various advantages. As Industry 4.0 continues to advance and as the digital, physical, and human realms become more integrated, reliability engineering must adapt to address present and future challenges. Integrating Industry 4.0 technologies has resulted in a new wave of advancements across diverse industry sectors, including railways. In South Africa, companies like PRASA and Transnet, both state-owned enterprises, are leveraging Industry 4.0 technology to optimize the reliability engineering of point machines and crucial railway infrastructure components. This research study aimed to investigate the current state of South Africa’s railway system and the potential 4.0 digital transformation on reliability engineering of the systems. While new technologies have been transformed based on industry 4.0 technologies, old and existing infrastructures are faced with possible upgrades through replacement or modifications. The study therefore investigates the possible 4.0 digital transformation of existing railway infrastructure to optimize the existing facilities (both in efficiency) and the cost of total replacement. The results showed that at a lower work rate of 20 or fewer operations per day, the efficiency of the Railway Point Machine is between 50% - 100%, and there is reduced total failure. At a much lower efficiency of the Railway Point Machine, there is an increased number of failures which indicates a slower performance of the Railway Point Machine. The study therefore indicated that 4.0 digital transformation could offer a solution for health monitoring of the RPM for improved reliability.

Sound Based Degradation Status Recognition for Railway Point Machines Based on Soft-Threshold Wavelet Denoising, WPD, and ReliefF

Sound Based Degradation Status Recognition for Railway Point Machines Based on Soft-Threshold Wavelet Denoising, WPD, and ReliefF

Vibration-Based Fault Diagnosis for Railway Point Machines Using Multi-Domain Features, Ensemble Feature Selection and SVM

Vibration-Based Fault Diagnosis for Railway Point Machines Using Multi-Domain Features, Ensemble Feature Selection and SVM

Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning

The majority of railway operators still implement conventional maintenance for railway point machines (RPMs), which is one of the most vital pieces of equipment for ensuring the safety of train operation. The conventional maintenance method lacks accuracy, is less efficient, and has high labor costs. This study developed a cost-effective and accurate fault diagnosis (FD) method based on current data to increase the overall efficiency of RPM maintenance. The FD method for RPM equipment discussed in this paper consists of three working conditions: normal, working, and failure. The method was proposed based on time-series current signals, which were gathered when the RPM was in operation. Time-series data were extracted and filtered using time-domain feature extraction based on scalable hypothesis testing. The selected features became the datasets for machine learning modeling. Six machine learning algorithms were compared in order to find the algorithm with the best FD accuracy. The results showed 100% accuracy for the Decision Tree and Random Forest algorithms in the FD method. The results of the FD method could be important for maintenance teams in determining suitable maintenance activities based on RPM working conditions.

Expert system based fault diagnosis for railway point machines

To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e., the “fault symptoms”). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g., past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets.

Entropy Feature Fusion-Based Diagnosis for Railway Point Machines Using Vibration Signals Based on Kernel Principal Component Analysis and Support Vector Machine

Entropy Feature Fusion-Based Diagnosis for Railway Point Machines Using Vibration Signals Based on Kernel Principal Component Analysis and Support Vector Machine

Predictive Maintenance of Railway Point Machine Using Machine Learning Algorithm

Predictive Maintenance of Railway Point Machine Using Machine Learning Algorithm

Fault diagnosis of ZDJ7 railway point machine based on improved DCNN and SVDD classification

AbstractProblems such as poor noise immunity and overfitting are prone to occur when convolutional neural network (CNN) is exploited in the fault diagnosis of ZDJ7 railway point machine. In addition, some fault features are unbalanced and have the features of multiple tags, which lead to low diagnosis accuracy. Therefore, an improved deep convolutional neural network (DCNN) and support vector data description (SVDD) classification is proposed. First, the depthwise separable convolution in the Xception structure is used to optimize the extraction of fault features. Second, the adaptive batch normalization processing (AdaBN) is performed to improve the noise immunity. Meanwhile, the global average pooling layer (GAP) is used instead of the fully connected layer to improve the generalization ability of the network. Aiming at the unbalanced features of the railway point machine sample, an improved quantity learning algorithm for hypersphere coordinate mapping based on SVDD is proposed. The classification is realized under unbalanced samples. The experiment shows that the accuracy based on the improved DCNN and SVDD is 96.59%. It has a good anti‐noise performance under different convolution kernels and SNRs. When the sample distribution is unbalanced, the performance indexes obtained by the proposed model are the best.

Generative Adversarial Network-based Fault Diagnosis Model for Railway Point Machine in Sustainable Railway Transportation

Generative Adversarial Network-based Fault Diagnosis Model for Railway Point Machine in Sustainable Railway Transportation

Generative adversarial network-based fault diagnosis model for railway point machine in sustainable railway transportation

Generative adversarial network-based fault diagnosis model for railway point machine in sustainable railway transportation

Research on fault diagnosis of a railway point machine based on a multi-entropy feature extraction method and support vector machine

Abstract A new fault diagnosis method is proposed to effectively extract the fault features of the sound signal of typical faults of ZDJ9 railway point machines. A multi-entropy feature extraction method is proposed by combing multi-scale permutation entropy and wavelet packet entropy. Firstly, empirical mode decomposition is performed on sound signals to obtain modal components with different time scales. Then, multi-scale permutation entropy is extracted from these components. Meanwhile, the wavelet packet entropy of the sound signals of these sensitive nodes is obtained by analysing the reconstructed signals of the last layer nodes. Since the multi-scale permutation entropy and the wavelet packet entropy can distinguish the subtle features of the signal, the subtle features of the original signal can be obtained as the feature vector of the ZDJ9 railway point machine in different states. To reduce the redundant information among the high-dimensional features, ReliefF is utilized. Finally, a support vector machine (SVM) is used to judge the fault type of a ZDJ9 railway point machine.

Modified multi-scale symbolic dynamic entropy and fuzzy broad learning-based fast fault diagnosis of railway point machines

Abstract Railway point machine (RPM) condition monitoring has attracted engineers’ attention for safe train operation and accident prevention. To realize the fast and accurate fault diagnosis of RPMs, this paper proposes a method based on entropy measurement and broad learning system (BLS). Firstly, the modified multi-scale symbolic dynamic entropy (MMSDE) module extracts dynamic characteristics from the collected acoustic signals as entropy features. Then, the fuzzy BLS takes the above entropy features as input to complete model training. Fuzzy BLS introduces the Takagi-Sugeno fuzzy system into BLS, which improves the model’s classification performance while considering computational speed. Experimental results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.

Condition monitoring and fault diagnosis strategy of railway point machines using vibration signals

Abstract Condition monitoring of railway point machines is important for train operation safety and effectiveness. Referring to the fields of mechanical equipment fault detection, this paper proposes a fault detection and identification strategy of railway point machines via vibration signals. A comprehensive feature distilling approach by combining variational mode decomposition (VMD) energy entropy and time- and frequency-domain statistical features is presented, which is more effective than single type of feature. The optimal set of features was selected with ReliefF, which helps improve the diagnosis accuracy. Support vector machine (SVM), which is suitable for a small sample, is adopted to realize diagnosis. The diagnosis accuracy of the proposed method reaches 100%, and its effectiveness is verified by experiment comparisons. In this paper, vibration signals are creatively adopted for fault diagnosis of railway point machines. The presented method can help guide field maintenance staff and also provide reference for fault diagnosis of other equipment.

Fault diagnosis of railway point machines based on wavelet transform and artificial immune algorithm

Abstract Aiming at the current problems of high failure rate and low diagnostic efficiency of railway point machines (RPMs) in the railway industry, a short-time method of fault diagnosis is proposed. Considering the effect of noise on power signals in the data acquisition process of the railway centralized signaling monitoring (CSM) system, this study utilizes wavelet threshold denoising to eliminate interference. The results show that the accuracy of fault diagnosis can be improved by 4.4% after denoising the power signals. Then in order to attain a lighter weight and shorten the running time of the diagnosis model, Mallat wavelet decomposition and artificial immune algorithm are applied to RPM fault diagnosis. Finally, voluminous experiments using veritable power signals collected from CSM are introduced, which show that combining these methods can procure higher precision of RPMs and curtail fault diagnosis time. This substantiates the validity and feasibility of the presented approach.

Data-driven technology of fault diagnosis in railway point machines: review and challenges

Abstract Safety and reliability are absolutely vital for sophisticated Railway Point Machines (RPMs). Hence, various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven technology. This paper firstly analyses and summarizes six RPMs’ characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past decade. It provides not only the process and evaluation metrics but also the pros and cons of these different methods. Ultimately, regarding the characteristics of RPMs and the existing studies, eight challenging problems and promising research directions are pointed out.